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growfunctions
View on CRAN: Click
here
Download and install growfunctions package within the R console
Install from CRAN:
install.packages("growfunctions")
Install from Github:
library("remotes")
install_github("cran/growfunctions") Install by package version:
library("remotes")
install_version("growfunctions", "0.17") Attach the package and use:
library("growfunctions")
Maintained by
Terrance Savitsky
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2014-08-09
Latest Update: 2023-12-12
Description:
Estimates a collection of time-indexed functions under
either of Gaussian process (GP) or intrinsic Gaussian Markov
random field (iGMRF) prior formulations where a Dirichlet process
mixture allows sub-groupings of the functions to share the same
covariance or precision parameters. The GP and iGMRF formulations
both support any number of additive covariance or precision terms,
respectively, expressing either or both of multiple trend and
seasonality.
How to cite:
Terrance Savitsky (2014). growfunctions: Bayesian Non-Parametric Dependent Models for Time-Indexed Functional Data. R package version 0.17, https://cran.r-project.org/web/packages/growfunctions. Accessed 26 Aug. 2026.
Previous versions and publish date:
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Complete documentation for growfunctions
Functions, R codes and Examples using
the growfunctions R package
Some associated functions: MSPE . cluster_plot . cps . fit_compare . gen_informative_sample . gmrfdpPost . gmrfdpcountPost . gmrfdpgrow . gpBFixPost . gpFixPost . gpPost . gpdpPost . gpdpbPost . gpdpgrow . growfunctions-package . informative_plot . plot_cluster . predict_functions.gmrfdpgrow . predict_functions.gpdpgrow . predict_functions . predict_plot . samples.gmrfdpgrow . samples .
Some associated R codes: MSPE.R . cps.R . gen_informative_sample.R . gmrfdpgrow.R . gp_car_fit_compare_facet.R . gp_cluster_plot.R . gp_informative_compare_plot.R . gpdpgrow.R . help.R . plot_cluster.R . predict_plot.R . Full growfunctions package functions and examples
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